International Journal of Engineering Technology and Management Sciences

2023, Volume 7 Issue 2

A SURVEY ON IDENTIFICATION AND DIAGNOSIS OF DISEASES USING MACHINE LEARNING

AUTHOR(S)

V.Madhumitha, Dr.P.Thirumoorthy

DOI: https://doi.org/10.46647/ijetms.2023.v07i02.074

ABSTRACT
The field of artificial intelligence to which machine learning belongs. We use machine learning methods like K-nearest neighbor(KNN), and Linear regression algorithm to detect and diagnose illnesses in this work. The dataset is trained using supervised learning, Reinforcement learning methods in order to construct a logical mathematical model. In the context of learning models, the datasets are employed for purposes such as data analysis and illness diagnosis. The purpose of the Disease Prediction using Machine Learning (ML) system is to make predictions about diseases based on the symptoms reported by patients or other users. The user inputs their symptoms, and the machine returns the likelihood that they have a certain ailment. In machine learning, disease prognosis relies on disease prediction.

Page No: 656 - 667

References:

[1] N. Vivaldi, M. Caiola, K. Solarana and M. Ye, "Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 11, pp. 3205-3216, Nov. 2021, doi: 10.1109/TBME.2021.3062502.
[2] S. Turco et al., "Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 5, pp. 1670-1681, May 2022, doi: 10.1109/TUFFC.2022.3161719.
[3] B. Deepa, M. Murugappan, M. G. Sumithra, M. Mahmud and M. S. Al-Rakhami, "Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm," in IEEE Access, vol. 10, pp. 3848-3863, 2022, doi: 10.1109/ACCESS.2021.3100549.
[4] T. M. Le, T. M. Vo, T. N. Pham and S. V. T. Dao, "A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic," in IEEE Access, vol. 9, pp. 7869-7884, 2021, doi: 10.1109/ACCESS.2020.3047942.
[5] M. F. Khan et al., "Reinforcing Synthetic Data for Meticulous Survival Prediction of Patients Suffering From Left Ventricular Systolic Dysfunction," in IEEE Access, vol. 9, pp. 72661-72669, 2021, doi: 10.1109/ACCESS.2021.3080617.
[6] M. Delli Priscoli et al., "Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms," in IEEE Journal of Selected Topics in Quantum Electronics, vol. 27, no. 5, pp. 1-9, Sept.-Oct. 2021, Art no. 5500309, doi: 10.1109/JSTQE.2021.3059532.
[7] M. K. Hasan et al., "Associating Measles Vaccine Uptake Classification and its Underlying Factors Using an Ensemble of Machine Learning Models," in IEEE Access, vol. 9, pp. 119613-119628, 2021, doi: 10.1109/ACCESS.2021.3108551.
[8] S. Maqsood, S. Xu, M. Springer and R. Mohawesh, "A Benchmark Study of Machine Learning for Analysis of Signal Feature Extraction Techniques for Blood Pressure Estimation Using Photoplethysmography (PPG)," in IEEE Access, vol. 9, pp. 138817-138833, 2021, doi: 10.1109/ACCESS.2021.3117969.
[9] X. Chen, S. Yu, Y. Zhang, F. Chu and B. Sun, "Machine Learning Method for Continuous Noninvasive Blood Pressure Detection Based on Random Forest," in IEEE Access, vol. 9, pp. 34112-34118, 2021, doi: 10.1109/ACCESS.2021.3062033.
[10] A. Rahim, Y. Rasheed, F. Azam, M. W. Anwar, M. A. Rahim and A. W. Muzaffar, "An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases," in IEEE Access, vol. 9, pp. 106575-106588, 2021, doi: 10.1109/ACCESS.2021.3098688.
[11] M. Rashed-Al-Mahfuz, A. Haque, A. Azad, S. A. Alyami, J. M. W. Quinn and M. A. Moni, "Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 9, pp. 1-11, 2021, Art no. 4900511, doi: 10.1109/JTEHM.2021.3073629.
[12] K. Shi, W. Lin and X. -M. Zhao, "Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 6, pp. 2514-2525, 1 Nov.-Dec. 2021, doi: 10.1109/TCBB.2020.2986387.
[13] D. Chicco and G. Jurman, "Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease," in IEEE Access, vol. 9, pp. 78648-78657, 2021, doi: 10.1109/ACCESS.2021.3084063.
[14] J. -G. Choi, I. Ko and S. Han, "Depression Level Classification Using Machine Learning Classifiers Based on Actigraphy Data," in IEEE Access, vol. 9, pp. 116622-116646, 2021, doi: 10.1109/ACCESS.2021.3105393.
[15] N. García-D’urso, P. Climent-Pérez, M. Sánchez-Sansegundo, A. Zaragoza-Martí, A. Fuster-Guilló and J. Azorín-López, "A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning," in IEEE Access, vol. 10, pp. 58566-58577, 2022, doi: 10.1109/ACCESS.2022.3178419.
[16] S. E. A. Ashri, M. M. El-Gayar and E. M. El-Daydamony, "HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm," in IEEE Access, vol. 9, pp. 146797-146809, 2021, doi: 10.1109/ACCESS.2021.3122789.
[17] S. Isci, D. S. Y. Kalender, F. Bayraktar and A. Yaman, "Machine Learning Models for Classification of Cushing's Syndrome Using Retrospective Data," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 8, pp. 3153-3162, Aug. 2021, doi: 10.1109/JBHI.2021.3054592.
[18] M. A. Awal, M. Masud, M. S. Hossain, A. A. -M. Bulbul, S. M. H. Mahmud and A. K. Bairagi, "A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data," in IEEE Access, vol. 9, pp. 10263-10281, 2021, doi: 10.1109/ACCESS.2021.3050852.
[19] G. Ye, V. Balasubramanian, J. K. -J. Li and M. Kaya, "Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-8, 2022, Art no. 4901008, doi: 10.1109/JTEHM.2022.3179874.
[20] U. Ahmed et al., "Prediction of Diabetes Empowered With Fused Machine Learning," in IEEE Access, vol. 10, pp. 8529-8538, 2022, doi: 10.1109/ACCESS.2022.3142097.
[21] P. Thirumoorthy, K.S. Bhuvaneshwari, C. Kamalanathan, P. Sunita, E. Prabhu et al., "Improved key agreement based kerberos protocol for m-health security," Computer Systems Science and Engineering, vol. 42, no.2, pp. 577–587, 2022.
[22] K.Shanmugapriya, C.N.Marimuthu, N.Sridhar, S.Sameema Begam,” Anomaly Detection of IoT Using Machine Learning”, International Journal of Mechanical Engineering, Vol. 6 No. 3 December, 2021.
[23] D.Vanathi, S.Prabhadevi, P.Sabarishamalathi, Mohanraj K P,” Machine Learning Based Collaborative Privacy-Preserving Intrusion Detection System for VANETs”, International Journal of Mechanical Engineering, Vol. 6 No. 3 December, 2021.
[24] Avadhesh Kumar Dixit, S Karuppusamy, Sonu Kumar, Jyothi N M,” Applications of IoT Principles in Healthcare”, International Journal of Biology, Pharmacy and Allied Sciences, .10, No.11,2021, pp. 2277-4998, 2021.
[25] Dr.D. Vanathi, P. Uma, M. Parvathi and K. Shanmugapriya,” Review of Recommendation System Methodologies”, International Journal of Psychosocial Rehabilitation”, Vol. 23, No.01, pp. 524-531,2019.

How to Cite This Article:
V.Madhumitha, Dr.P.Thirumoorthy . A SURVEY ON IDENTIFICATION AND DIAGNOSIS OF DISEASES USING MACHINE LEARNING . ijetms;7(2):656-667. DOI: 10.46647/ijetms.2023.v07i02.074